We study the optimal control of multiple video streams over a wireless downlink from a base-transceiver-station (BTS)/access point to N end-devices (EDs). The BTS sends video packets to each ED under a joint transmission energy constraint, the EDs choose when to play out the received packets, and the collective goal is to provide a high Quality-of-Experience (QoE) to the clients/end-users. All EDs send feedback about their states and actions to the BTS which reaches it after a fixed deterministic delay. We analyze this team problem with delayed feedback as a cooperative Multi-Agent Constrained Partially Observable Markov Decision Process (MA-C-POMDP).First, using a recently established strong duality result for MAC-POMDPs, the original problem is decomposed into N independent unconstrained transmitter-receiver (two-agent) problems— all sharing a Lagrange multiplier (that also needs to be optimized for optimal control). Thereafter, the common information (CI) approach and the formalism of approximate information states (AISs) are used to guide the design of a neural-network based architecture for learning-based multi-agent control in a single unconstrained transmitter-receiver problem. Finally, simulations on a single transmitter-receiver pair with a stylized QoE model are performed to highlight the advantage of delay-aware two-agent coordination over the transmitter choosing both transmission and play-out actions (perceiving the delayed state of the receiver as its current state).

Divide and Conquer is a well-established approach in the literature that has efficiently solved a variety of problems. However, it is yet to be explored in full in solving image super-resolution. To predict a sharp up-sampled image, this work proposes a divide and conquer approach based wide and deep network (WDN) that divides the 4× up-sampling problem into 32 disjoint subproblems that can be solved simultaneously and independently of each other Half of these subproblems deal with predicting the overall features of the high-resolution image, while the remaining are exclusively for predicting the finer details. Additionally, a technique that is found to be more effective in calibrating the pixel intensities has been proposed. Results obtained on multiple datasets demonstrate the improved performance of the proposed wide and deep network over state-of-the-art methods.